Overview

Dataset statistics

Number of variables33
Number of observations95042
Missing cells315780
Missing cells (%)10.1%
Duplicate rows2824
Duplicate rows (%)3.0%
Total size in memory26.7 MiB
Average record size in memory294.2 B

Variable types

Categorical6
Numeric21
DateTime5
Text1

Alerts

ThrowHand has constant value ""Constant
ASLRCoreStabilityLeft has constant value ""Constant
Dataset has 2824 (3.0%) duplicate rowsDuplicates
PitchId is highly overall correlated with HipChestSeparationPeakHigh correlation
Velocity is highly overall correlated with HorizontalBreak and 1 other fieldsHigh correlation
HorizontalBreak is highly overall correlated with Velocity and 1 other fieldsHigh correlation
InducedVerticalBreak is highly overall correlated with Velocity and 1 other fieldsHigh correlation
KneeStrideFlexionFC is highly overall correlated with HipExternalRotatationPassiveLeft and 3 other fieldsHigh correlation
KneeStrideFlexionAngVeloPeak is highly overall correlated with ASLRCoreStabilityRightHigh correlation
ShoulderThrowRotationMER is highly overall correlated with HipExternalRotatationPassiveLeft and 3 other fieldsHigh correlation
ElbowThrowFlexionFC is highly overall correlated with RightAvgBrakingForceNewtons and 2 other fieldsHigh correlation
HipChestSeparationPeak is highly overall correlated with PitchIdHigh correlation
GripLeft is highly overall correlated with GripRight and 5 other fieldsHigh correlation
GripRight is highly overall correlated with GripLeft and 4 other fieldsHigh correlation
HipExternalRotatationPassiveLeft is highly overall correlated with KneeStrideFlexionFC and 5 other fieldsHigh correlation
HipExternalRotatationPassiveRight is highly overall correlated with ShoulderThrowRotationMER and 8 other fieldsHigh correlation
ShoulderExternalRotatationStrengthLeft is highly overall correlated with ShoulderThrowRotationMER and 7 other fieldsHigh correlation
ShoulderExternalRotatationStrengthRight is highly overall correlated with ShoulderThrowRotationMER and 7 other fieldsHigh correlation
AvgBrakingForceNewtons is highly overall correlated with PeakPropulsiveForceNewtons and 4 other fieldsHigh correlation
PeakPropulsiveForceNewtons is highly overall correlated with KneeStrideFlexionFC and 4 other fieldsHigh correlation
LeftAvgBrakingForceNewtons is highly overall correlated with KneeStrideFlexionFC and 6 other fieldsHigh correlation
RightAvgBrakingForceNewtons is highly overall correlated with ElbowThrowFlexionFC and 3 other fieldsHigh correlation
PlayerCode is highly overall correlated with ElbowThrowFlexionFC and 10 other fieldsHigh correlation
PitchType is highly overall correlated with InducedVerticalBreakHigh correlation
CombinedDiagnosis is highly overall correlated with ElbowThrowFlexionFC and 8 other fieldsHigh correlation
ASLRCoreStabilityRight is highly overall correlated with HorizontalBreak and 11 other fieldsHigh correlation
GripLeft has 1152 (1.2%) missing valuesMissing
GripRight has 1152 (1.2%) missing valuesMissing
HipExternalRotatationPassiveLeft has 30685 (32.3%) missing valuesMissing
HipExternalRotatationPassiveRight has 30685 (32.3%) missing valuesMissing
ShoulderExternalRotatationStrengthLeft has 30685 (32.3%) missing valuesMissing
ShoulderExternalRotatationStrengthRight has 30685 (32.3%) missing valuesMissing
ASLRCoreStabilityLeft has 42934 (45.2%) missing valuesMissing
ASLRCoreStabilityRight has 42934 (45.2%) missing valuesMissing
AvgBrakingForceNewtons has 20478 (21.5%) missing valuesMissing
PeakPropulsiveForceNewtons has 11478 (12.1%) missing valuesMissing
JumpHeightMeters has 11478 (12.1%) missing valuesMissing
ImpulseRatio has 20478 (21.5%) missing valuesMissing
LeftAvgBrakingForceNewtons has 20478 (21.5%) missing valuesMissing
RightAvgBrakingForceNewtons has 20478 (21.5%) missing valuesMissing

Reproduction

Analysis started2023-10-31 03:29:23.309918
Analysis finished2023-10-31 03:30:08.189463
Duration44.88 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

PlayerCode
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
KYOF
25300 
VX9I
23712 
VR1T
7920 
MR7S
7808 
LI18
6800 
Other values (6)
23502 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters380168
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVR1T
2nd rowVR1T
3rd rowVR1T
4th rowVR1T
5th rowVR1T

Common Values

ValueCountFrequency (%)
KYOF 25300
26.6%
VX9I 23712
24.9%
VR1T 7920
 
8.3%
MR7S 7808
 
8.2%
LI18 6800
 
7.2%
L8DC 6360
 
6.7%
2D7B 5760
 
6.1%
63FZ 3960
 
4.2%
L6I5 3444
 
3.6%
YXS2 2178
 
2.3%

Length

2023-10-30T20:30:08.252171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kyof 25300
26.6%
vx9i 23712
24.9%
vr1t 7920
 
8.3%
mr7s 7808
 
8.2%
li18 6800
 
7.2%
l8dc 6360
 
6.7%
2d7b 5760
 
6.1%
63fz 3960
 
4.2%
l6i5 3444
 
3.6%
yxs2 2178
 
2.3%

Most occurring characters

ValueCountFrequency (%)
I 33956
 
8.9%
V 33432
 
8.8%
F 29260
 
7.7%
X 27690
 
7.3%
Y 27478
 
7.2%
K 25300
 
6.7%
O 25300
 
6.7%
9 23712
 
6.2%
L 18404
 
4.8%
R 15728
 
4.1%
Other values (15) 119908
31.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 292262
76.9%
Decimal Number 87906
 
23.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 33956
11.6%
V 33432
11.4%
F 29260
10.0%
X 27690
9.5%
Y 27478
9.4%
K 25300
8.7%
O 25300
8.7%
L 18404
6.3%
R 15728
 
5.4%
D 12120
 
4.1%
Other values (7) 43594
14.9%
Decimal Number
ValueCountFrequency (%)
9 23712
27.0%
1 14720
16.7%
7 13568
15.4%
8 13160
15.0%
2 7938
 
9.0%
6 7404
 
8.4%
3 3960
 
4.5%
5 3444
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 292262
76.9%
Common 87906
 
23.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 33956
11.6%
V 33432
11.4%
F 29260
10.0%
X 27690
9.5%
Y 27478
9.4%
K 25300
8.7%
O 25300
8.7%
L 18404
6.3%
R 15728
 
5.4%
D 12120
 
4.1%
Other values (7) 43594
14.9%
Common
ValueCountFrequency (%)
9 23712
27.0%
1 14720
16.7%
7 13568
15.4%
8 13160
15.0%
2 7938
 
9.0%
6 7404
 
8.4%
3 3960
 
4.5%
5 3444
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 380168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 33956
 
8.9%
V 33432
 
8.8%
F 29260
 
7.7%
X 27690
 
7.3%
Y 27478
 
7.2%
K 25300
 
6.7%
O 25300
 
6.7%
9 23712
 
6.2%
L 18404
 
4.8%
R 15728
 
4.1%
Other values (15) 119908
31.5%

PitchId
Real number (ℝ)

HIGH CORRELATION 

Distinct456
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.322826
Minimum1
Maximum456
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-10-30T20:30:08.328272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q118
median46
Q399
95-th percentile365
Maximum456
Range455
Interquartile range (IQR)81

Descriptive statistics

Standard deviation108.15067
Coefficient of variation (CV)1.2385155
Kurtosis2.5771217
Mean87.322826
Median Absolute Deviation (MAD)33
Skewness1.872181
Sum8299336
Variance11696.568
MonotonicityNot monotonic
2023-10-30T20:30:08.414005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 1366
 
1.4%
1 1366
 
1.4%
15 1366
 
1.4%
14 1366
 
1.4%
13 1366
 
1.4%
12 1366
 
1.4%
11 1366
 
1.4%
8 1366
 
1.4%
4 1366
 
1.4%
3 1366
 
1.4%
Other values (446) 81382
85.6%
ValueCountFrequency (%)
1 1366
1.4%
2 1366
1.4%
3 1366
1.4%
4 1366
1.4%
5 1366
1.4%
6 1366
1.4%
7 1366
1.4%
8 1366
1.4%
9 1366
1.4%
10 1366
1.4%
ValueCountFrequency (%)
456 52
0.1%
455 52
0.1%
454 52
0.1%
453 52
0.1%
452 52
0.1%
451 52
0.1%
450 52
0.1%
449 52
0.1%
448 52
0.1%
447 52
0.1%
Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
Minimum2023-03-14 00:00:00
Maximum2023-07-29 00:00:00
2023-10-30T20:30:08.486929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:08.555321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
Distinct1042
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
2023-10-30T20:30:08.768361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters855378
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 23:25:11
2nd row 23:25:11
3rd row 23:25:11
4th row 23:25:11
5th row 23:25:11
ValueCountFrequency (%)
19:17:06 392
 
0.4%
19:01:03 340
 
0.4%
19:01:41 340
 
0.4%
19:15:56 340
 
0.4%
19:02:44 340
 
0.4%
19:03:01 340
 
0.4%
19:14:38 340
 
0.4%
19:14:53 340
 
0.4%
19:16:25 340
 
0.4%
19:00:49 340
 
0.4%
Other values (1032) 91590
96.4%
2023-10-30T20:30:09.038716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 190084
22.2%
1 123680
14.5%
0 121288
14.2%
95042
11.1%
2 75208
 
8.8%
4 53766
 
6.3%
3 45168
 
5.3%
5 42788
 
5.0%
7 34586
 
4.0%
9 26744
 
3.1%
Other values (2) 47024
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 570252
66.7%
Other Punctuation 190084
 
22.2%
Space Separator 95042
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 123680
21.7%
0 121288
21.3%
2 75208
13.2%
4 53766
9.4%
3 45168
 
7.9%
5 42788
 
7.5%
7 34586
 
6.1%
9 26744
 
4.7%
6 26490
 
4.6%
8 20534
 
3.6%
Other Punctuation
ValueCountFrequency (%)
: 190084
100.0%
Space Separator
ValueCountFrequency (%)
95042
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 855378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 190084
22.2%
1 123680
14.5%
0 121288
14.2%
95042
11.1%
2 75208
 
8.8%
4 53766
 
6.3%
3 45168
 
5.3%
5 42788
 
5.0%
7 34586
 
4.0%
9 26744
 
3.1%
Other values (2) 47024
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 855378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 190084
22.2%
1 123680
14.5%
0 121288
14.2%
95042
11.1%
2 75208
 
8.8%
4 53766
 
6.3%
3 45168
 
5.3%
5 42788
 
5.0%
7 34586
 
4.0%
9 26744
 
3.1%
Other values (2) 47024
 
5.5%

ThrowHand
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
R
95042 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters95042
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR
2nd rowR
3rd rowR
4th rowR
5th rowR

Common Values

ValueCountFrequency (%)
R 95042
100.0%

Length

2023-10-30T20:30:09.124973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-30T20:30:09.184670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
r 95042
100.0%

Most occurring characters

ValueCountFrequency (%)
R 95042
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 95042
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 95042
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 95042
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 95042
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95042
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 95042
100.0%

PitchType
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
FB
45550 
SL
26188 
CH
12234 
CB
 
3968
CT
 
3322
Other values (2)
 
3780

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters190084
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCB
2nd rowCB
3rd rowCB
4th rowCB
5th rowCB

Common Values

ValueCountFrequency (%)
FB 45550
47.9%
SL 26188
27.6%
CH 12234
 
12.9%
CB 3968
 
4.2%
CT 3322
 
3.5%
SP 2488
 
2.6%
SI 1292
 
1.4%

Length

2023-10-30T20:30:09.236839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-30T20:30:09.311510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
fb 45550
47.9%
sl 26188
27.6%
ch 12234
 
12.9%
cb 3968
 
4.2%
ct 3322
 
3.5%
sp 2488
 
2.6%
si 1292
 
1.4%

Most occurring characters

ValueCountFrequency (%)
B 49518
26.1%
F 45550
24.0%
S 29968
15.8%
L 26188
13.8%
C 19524
 
10.3%
H 12234
 
6.4%
T 3322
 
1.7%
P 2488
 
1.3%
I 1292
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 190084
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 49518
26.1%
F 45550
24.0%
S 29968
15.8%
L 26188
13.8%
C 19524
 
10.3%
H 12234
 
6.4%
T 3322
 
1.7%
P 2488
 
1.3%
I 1292
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 190084
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 49518
26.1%
F 45550
24.0%
S 29968
15.8%
L 26188
13.8%
C 19524
 
10.3%
H 12234
 
6.4%
T 3322
 
1.7%
P 2488
 
1.3%
I 1292
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190084
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 49518
26.1%
F 45550
24.0%
S 29968
15.8%
L 26188
13.8%
C 19524
 
10.3%
H 12234
 
6.4%
T 3322
 
1.7%
P 2488
 
1.3%
I 1292
 
0.7%

Velocity
Real number (ℝ)

HIGH CORRELATION 

Distinct1053
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.333556
Minimum72.978328
Maximum99.148787
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-10-30T20:30:09.387500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum72.978328
5-th percentile78.431176
Q183.471159
median88.840871
Q393.56152
95-th percentile96.348111
Maximum99.148787
Range26.170458
Interquartile range (IQR)10.090361

Descriptive statistics

Standard deviation5.9153988
Coefficient of variation (CV)0.06696661
Kurtosis-1.0436641
Mean88.333556
Median Absolute Deviation (MAD)4.97936
Skewness-0.32228539
Sum8395397.8
Variance34.991943
MonotonicityNot monotonic
2023-10-30T20:30:09.474217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.33348237 340
 
0.4%
94.39910801 340
 
0.4%
83.36131369 340
 
0.4%
78.91203183 340
 
0.4%
78.09569221 340
 
0.4%
79.10388061 340
 
0.4%
80.64740763 340
 
0.4%
94.98734402 340
 
0.4%
93.38015731 340
 
0.4%
94.67286616 340
 
0.4%
Other values (1043) 91642
96.4%
ValueCountFrequency (%)
72.97832819 52
 
0.1%
73.3756951 132
0.1%
73.73102889 66
0.1%
73.93801915 52
 
0.1%
74.23026138 132
0.1%
74.27116216 42
 
< 0.1%
74.3649345 42
 
< 0.1%
74.48682358 42
 
< 0.1%
74.5325875 42
 
< 0.1%
74.56593757 42
 
< 0.1%
ValueCountFrequency (%)
99.14878666 220
0.2%
98.71237515 220
0.2%
97.95939793 220
0.2%
97.89205439 220
0.2%
97.83524004 220
0.2%
97.66319837 220
0.2%
97.5339045 220
0.2%
97.42026504 220
0.2%
97.37319954 220
0.2%
97.07269187 220
0.2%

HorizontalBreak
Real number (ℝ)

HIGH CORRELATION 

Distinct1053
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.6830306
Minimum-18.794367
Maximum21.133459
Zeros0
Zeros (%)0.0%
Negative63150
Negative (%)66.4%
Memory size3.5 MiB
2023-10-30T20:30:09.565103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-18.794367
5-th percentile-14.440561
Q1-10.721444
median-7.1875531
Q34.3119213
95-th percentile14.761125
Maximum21.133459
Range39.927827
Interquartile range (IQR)15.033366

Descriptive statistics

Standard deviation9.3039505
Coefficient of variation (CV)-2.5261671
Kurtosis-0.50779387
Mean-3.6830306
Median Absolute Deviation (MAD)5.2315333
Skewness0.7691506
Sum-350042.59
Variance86.563496
MonotonicityNot monotonic
2023-10-30T20:30:09.655171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.896056285 340
 
0.4%
-10.74023031 340
 
0.4%
-13.61554148 340
 
0.4%
15.28428694 340
 
0.4%
18.8324387 340
 
0.4%
20.15313754 340
 
0.4%
17.30282201 340
 
0.4%
-8.502931871 340
 
0.4%
-10.35187193 340
 
0.4%
-5.57453235 340
 
0.4%
Other values (1043) 91642
96.4%
ValueCountFrequency (%)
-18.79436746 52
0.1%
-18.60369175 52
0.1%
-17.98819157 52
0.1%
-17.63232952 90
0.1%
-17.17232107 64
0.1%
-17.15739465 64
0.1%
-17.05320478 90
0.1%
-16.9571936 52
0.1%
-16.95418047 52
0.1%
-16.92990206 64
0.1%
ValueCountFrequency (%)
21.13345949 64
 
0.1%
21.06025677 64
 
0.1%
20.15313754 340
0.4%
20.00475852 64
 
0.1%
19.91527574 52
 
0.1%
19.53541234 52
 
0.1%
19.42360209 52
 
0.1%
19.28394065 52
 
0.1%
19.21520819 52
 
0.1%
19.21129625 64
 
0.1%

InducedVerticalBreak
Real number (ℝ)

HIGH CORRELATION 

Distinct1053
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9514418
Minimum-17.67044
Maximum22.084603
Zeros0
Zeros (%)0.0%
Negative10808
Negative (%)11.4%
Memory size3.5 MiB
2023-10-30T20:30:09.743969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-17.67044
5-th percentile-2.820641
Q12.7801263
median10.464638
Q315.72881
95-th percentile18.548724
Maximum22.084603
Range39.755042
Interquartile range (IQR)12.948684

Descriptive statistics

Standard deviation7.7292154
Coefficient of variation (CV)0.86346039
Kurtosis-0.37796554
Mean8.9514418
Median Absolute Deviation (MAD)6.1408416
Skewness-0.55886141
Sum850762.93
Variance59.740771
MonotonicityNot monotonic
2023-10-30T20:30:09.825301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.891417633 340
 
0.4%
16.98476642 340
 
0.4%
1.7591018 340
 
0.4%
0.74178395 340
 
0.4%
1.876018994 340
 
0.4%
-0.135276658 340
 
0.4%
0.215815711 340
 
0.4%
17.96016004 340
 
0.4%
15.61197169 340
 
0.4%
17.43519644 340
 
0.4%
Other values (1043) 91642
96.4%
ValueCountFrequency (%)
-17.67043959 42
< 0.1%
-17.64386483 42
< 0.1%
-16.85797141 42
< 0.1%
-16.63686142 42
< 0.1%
-16.61134993 42
< 0.1%
-16.30499014 42
< 0.1%
-16.1124281 42
< 0.1%
-16.07494605 42
< 0.1%
-15.82414246 42
< 0.1%
-15.5481647 42
< 0.1%
ValueCountFrequency (%)
22.08460289 42
 
< 0.1%
21.84897302 42
 
< 0.1%
21.7632121 42
 
< 0.1%
21.39881682 42
 
< 0.1%
21.34508792 42
 
< 0.1%
21.26452317 42
 
< 0.1%
21.15786485 42
 
< 0.1%
21.10395722 42
 
< 0.1%
21.01519069 42
 
< 0.1%
21.0040342 132
0.1%

KneeStrideFlexionFC
Real number (ℝ)

HIGH CORRELATION 

Distinct1053
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.987206
Minimum9.3091801
Maximum75.692113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-10-30T20:30:09.913933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum9.3091801
5-th percentile36.843871
Q142.388955
median47.566726
Q352.370854
95-th percentile68.145266
Maximum75.692113
Range66.382933
Interquartile range (IQR)9.9818994

Descriptive statistics

Standard deviation8.9708799
Coefficient of variation (CV)0.18694316
Kurtosis3.0439268
Mean47.987206
Median Absolute Deviation (MAD)4.9657651
Skewness0.057832883
Sum4560800
Variance80.476687
MonotonicityNot monotonic
2023-10-30T20:30:09.995225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.60107566 340
 
0.4%
42.12542514 340
 
0.4%
37.91895928 340
 
0.4%
41.18841586 340
 
0.4%
42.76237021 340
 
0.4%
45.36839465 340
 
0.4%
41.93306839 340
 
0.4%
43.96500983 340
 
0.4%
43.45838889 340
 
0.4%
42.86583298 340
 
0.4%
Other values (1043) 91642
96.4%
ValueCountFrequency (%)
9.309180128 120
0.1%
10.38673586 120
0.1%
10.52318989 120
0.1%
10.95683395 120
0.1%
11.03512562 120
0.1%
12.49366776 120
0.1%
12.97232718 120
0.1%
15.36911733 132
0.1%
16.11687343 120
0.1%
22.84040203 120
0.1%
ValueCountFrequency (%)
75.69211275 120
0.1%
73.91496583 120
0.1%
73.30660204 120
0.1%
73.11067277 120
0.1%
72.92910601 120
0.1%
72.5716523 120
0.1%
72.28075618 120
0.1%
72.26505173 120
0.1%
72.0189729 120
0.1%
71.78930669 120
0.1%

KneeStrideFlexionAngVeloPeak
Real number (ℝ)

HIGH CORRELATION 

Distinct1053
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean327.79342
Minimum57.464634
Maximum570.29009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-10-30T20:30:10.571073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum57.464634
5-th percentile110.35308
Q1179.47265
median372.96443
Q3437.31035
95-th percentile489.51647
Maximum570.29009
Range512.82545
Interquartile range (IQR)257.8377

Descriptive statistics

Standard deviation132.81186
Coefficient of variation (CV)0.40516938
Kurtosis-1.2268773
Mean327.79342
Median Absolute Deviation (MAD)87.03613
Skewness-0.48130342
Sum31154142
Variance17638.99
MonotonicityNot monotonic
2023-10-30T20:30:10.664671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
425.3177926 340
 
0.4%
423.3040349 340
 
0.4%
310.4663411 340
 
0.4%
376.3827404 340
 
0.4%
407.0948055 340
 
0.4%
381.8398385 340
 
0.4%
429.202192 340
 
0.4%
431.2571062 340
 
0.4%
405.9550023 340
 
0.4%
389.2532385 340
 
0.4%
Other values (1043) 91642
96.4%
ValueCountFrequency (%)
57.46463413 52
0.1%
57.89458193 52
0.1%
61.96915867 52
0.1%
63.31332326 52
0.1%
64.44129464 52
0.1%
68.38760054 52
0.1%
69.95677683 52
0.1%
73.09595299 52
0.1%
74.12890952 52
0.1%
74.58146618 52
0.1%
ValueCountFrequency (%)
570.290089 120
0.1%
525.5223302 64
0.1%
523.4546323 64
0.1%
523.4480069 64
0.1%
521.1360946 64
0.1%
513.1754925 132
0.1%
512.4414222 64
0.1%
511.9269119 64
0.1%
511.1688117 64
0.1%
510.8026404 132
0.1%

ShoulderThrowRotationMER
Real number (ℝ)

HIGH CORRELATION 

Distinct1053
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-186.70659
Minimum-539.81899
Maximum-169.21188
Zeros0
Zeros (%)0.0%
Negative95042
Negative (%)100.0%
Memory size3.5 MiB
2023-10-30T20:30:10.747881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-539.81899
5-th percentile-197.85158
Q1-193.30409
median-185.76582
Q3-178.9733
95-th percentile-173.84753
Maximum-169.21188
Range370.60711
Interquartile range (IQR)14.330785

Descriptive statistics

Standard deviation18.831072
Coefficient of variation (CV)-0.10085917
Kurtosis283.27755
Mean-186.70659
Median Absolute Deviation (MAD)7.107305
Skewness-15.236211
Sum-17744967
Variance354.60927
MonotonicityNot monotonic
2023-10-30T20:30:10.832569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200.0959805 340
 
0.4%
-196.4578262 340
 
0.4%
-200.4134829 340
 
0.4%
-197.584508 340
 
0.4%
-196.4787711 340
 
0.4%
-195.4320256 340
 
0.4%
-196.2680955 340
 
0.4%
-195.7943127 340
 
0.4%
-196.5827911 340
 
0.4%
-196.6482332 340
 
0.4%
Other values (1043) 91642
96.4%
ValueCountFrequency (%)
-539.8189892 220
0.2%
-200.5612007 340
0.4%
-200.4134829 340
0.4%
-200.0959805 340
0.4%
-200.0909387 120
 
0.1%
-199.9868919 120
 
0.1%
-199.9797987 340
0.4%
-199.8705626 120
 
0.1%
-199.8157283 120
 
0.1%
-199.7577872 120
 
0.1%
ValueCountFrequency (%)
-169.2118784 220
0.2%
-169.4658233 220
0.2%
-169.4987856 220
0.2%
-169.5282257 220
0.2%
-169.6090537 220
0.2%
-169.8593487 220
0.2%
-169.8717347 220
0.2%
-170.5268925 220
0.2%
-170.6708417 220
0.2%
-171.3102729 220
0.2%

ElbowThrowFlexionFC
Real number (ℝ)

HIGH CORRELATION 

Distinct1053
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.15269
Minimum36.280024
Maximum132.87466
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-10-30T20:30:10.925192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum36.280024
5-th percentile75.962344
Q199.834176
median104.37227
Q3110.77331
95-th percentile123.27669
Maximum132.87466
Range96.59464
Interquartile range (IQR)10.939138

Descriptive statistics

Standard deviation13.185801
Coefficient of variation (CV)0.12660067
Kurtosis4.2662608
Mean104.15269
Median Absolute Deviation (MAD)5.5282472
Skewness-1.3250208
Sum9898880.3
Variance173.86534
MonotonicityNot monotonic
2023-10-30T20:30:11.007087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.2013749 340
 
0.4%
126.5273887 340
 
0.4%
115.4467846 340
 
0.4%
121.2067144 340
 
0.4%
120.7001136 340
 
0.4%
118.8954222 340
 
0.4%
119.8885413 340
 
0.4%
122.5455023 340
 
0.4%
122.9857598 340
 
0.4%
123.6093102 340
 
0.4%
Other values (1043) 91642
96.4%
ValueCountFrequency (%)
36.28002445 120
0.1%
39.81479444 120
0.1%
40.90524042 120
0.1%
41.49560719 120
0.1%
44.17032651 120
0.1%
46.37422484 120
0.1%
46.71587275 120
0.1%
47.45992523 120
0.1%
62.27660524 132
0.1%
69.00489528 132
0.1%
ValueCountFrequency (%)
132.8746644 66
 
0.1%
132.4201888 66
 
0.1%
132.1252499 66
 
0.1%
130.7809855 66
 
0.1%
130.2428992 66
 
0.1%
130.1222961 66
 
0.1%
129.3875599 66
 
0.1%
128.5953864 66
 
0.1%
128.4581793 340
0.4%
128.4553463 66
 
0.1%

HipChestSeparationPeak
Real number (ℝ)

HIGH CORRELATION 

Distinct1053
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.594893
Minimum37.206183
Maximum75.69585
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-10-30T20:30:11.092159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum37.206183
5-th percentile44.522024
Q148.420697
median51.95438
Q356.226778
95-th percentile71.085872
Maximum75.69585
Range38.489668
Interquartile range (IQR)7.8060808

Descriptive statistics

Standard deviation7.6528219
Coefficient of variation (CV)0.14279013
Kurtosis0.87810083
Mean53.594893
Median Absolute Deviation (MAD)3.7867977
Skewness1.1246722
Sum5093765.8
Variance58.565684
MonotonicityNot monotonic
2023-10-30T20:30:11.176679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.2613651 340
 
0.4%
69.09167027 340
 
0.4%
67.18938992 340
 
0.4%
71.04304357 340
 
0.4%
69.84085946 340
 
0.4%
73.62531791 340
 
0.4%
70.12677511 340
 
0.4%
67.37024002 340
 
0.4%
66.72778598 340
 
0.4%
70.80499783 340
 
0.4%
Other values (1043) 91642
96.4%
ValueCountFrequency (%)
37.20618266 42
 
< 0.1%
37.51502486 52
 
0.1%
37.53300215 66
0.1%
37.64517252 42
 
< 0.1%
37.66812798 42
 
< 0.1%
37.92720995 132
0.1%
38.14216163 42
 
< 0.1%
38.25444343 42
 
< 0.1%
39.27494879 66
0.1%
39.40267051 42
 
< 0.1%
ValueCountFrequency (%)
75.69585031 340
0.4%
75.26196662 120
 
0.1%
75.2613651 340
0.4%
74.44280813 120
 
0.1%
74.39353871 120
 
0.1%
74.33224635 120
 
0.1%
73.62531791 340
0.4%
73.53731837 120
 
0.1%
73.36384562 120
 
0.1%
73.27537999 120
 
0.1%
Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
Minimum2023-03-17 00:00:00
Maximum2023-08-08 00:00:00
2023-10-30T20:30:11.247371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:11.310445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)

CombinedDiagnosis
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
R Arm/Elbow Strain
41380 
L Pelvis/Hips Strain
23712 
R Shoulder Impingement Syndrome
11252 
R Arm/Elbow Tendinitis
7920 
R Shoulder Tendinitis
6800 
Other values (2)
 
3978

Length

Max length31
Median length22
Mean length20.544159
Min length17

Characters and Unicode

Total characters1952558
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR Arm/Elbow Tendinitis
2nd rowR Arm/Elbow Tendinitis
3rd rowR Arm/Elbow Tendinitis
4th rowR Arm/Elbow Tendinitis
5th rowR Arm/Elbow Tendinitis

Common Values

ValueCountFrequency (%)
R Arm/Elbow Strain 41380
43.5%
L Pelvis/Hips Strain 23712
24.9%
R Shoulder Impingement Syndrome 11252
 
11.8%
R Arm/Elbow Tendinitis 7920
 
8.3%
R Shoulder Tendinitis 6800
 
7.2%
O Head Concussion 2178
 
2.3%
R Shoulder Strain 1800
 
1.9%

Length

2023-10-30T20:30:11.393965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-30T20:30:11.473911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
r 69152
23.3%
strain 66892
22.6%
arm/elbow 49300
16.6%
l 23712
 
8.0%
pelvis/hips 23712
 
8.0%
shoulder 19852
 
6.7%
tendinitis 14720
 
5.0%
impingement 11252
 
3.8%
syndrome 11252
 
3.8%
o 2178
 
0.7%
Other values (2) 4356
 
1.5%

Most occurring characters

ValueCountFrequency (%)
201336
 
10.3%
i 171906
 
8.8%
r 147296
 
7.5%
n 134444
 
6.9%
S 97996
 
5.0%
e 94218
 
4.8%
l 92864
 
4.8%
t 92864
 
4.8%
o 84760
 
4.3%
m 83056
 
4.3%
Other values (23) 751818
38.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1308820
67.0%
Uppercase Letter 369390
 
18.9%
Space Separator 201336
 
10.3%
Other Punctuation 73012
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 171906
13.1%
r 147296
11.3%
n 134444
10.3%
e 94218
 
7.2%
l 92864
 
7.1%
t 92864
 
7.1%
o 84760
 
6.5%
m 83056
 
6.3%
a 69070
 
5.3%
s 66500
 
5.1%
Other values (10) 271842
20.8%
Uppercase Letter
ValueCountFrequency (%)
S 97996
26.5%
R 69152
18.7%
E 49300
13.3%
A 49300
13.3%
H 25890
 
7.0%
L 23712
 
6.4%
P 23712
 
6.4%
T 14720
 
4.0%
I 11252
 
3.0%
O 2178
 
0.6%
Space Separator
ValueCountFrequency (%)
201336
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 73012
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1678210
85.9%
Common 274348
 
14.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 171906
 
10.2%
r 147296
 
8.8%
n 134444
 
8.0%
S 97996
 
5.8%
e 94218
 
5.6%
l 92864
 
5.5%
t 92864
 
5.5%
o 84760
 
5.1%
m 83056
 
4.9%
R 69152
 
4.1%
Other values (21) 609654
36.3%
Common
ValueCountFrequency (%)
201336
73.4%
/ 73012
 
26.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1952558
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
201336
 
10.3%
i 171906
 
8.8%
r 147296
 
7.5%
n 134444
 
6.9%
S 97996
 
5.0%
e 94218
 
4.8%
l 92864
 
4.8%
t 92864
 
4.8%
o 84760
 
4.3%
m 83056
 
4.3%
Other values (23) 751818
38.5%

GripLeft
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)< 0.1%
Missing1152
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean160.52129
Minimum95.700798
Maximum197.96468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-10-30T20:30:11.560213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum95.700798
5-th percentile121.64869
Q1145.75553
median161.43392
Q3180.74197
95-th percentile197.96468
Maximum197.96468
Range102.26388
Interquartile range (IQR)34.986439

Descriptive statistics

Standard deviation23.2549
Coefficient of variation (CV)0.14487113
Kurtosis-0.090735323
Mean160.52129
Median Absolute Deviation (MAD)19.308056
Skewness-0.39476104
Sum15071344
Variance540.79036
MonotonicityNot monotonic
2023-10-30T20:30:11.641499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
167.4976716 5928
 
6.2%
156.1285345 5928
 
6.2%
174.7898983 5928
 
6.2%
165.5584764 5928
 
6.2%
180.7419731 5060
 
5.3%
184.0925037 5060
 
5.3%
190.6122798 5060
 
5.3%
191.9997559 5060
 
5.3%
197.9646832 5060
 
5.3%
157.5092133 2640
 
2.8%
Other values (33) 42238
44.4%
ValueCountFrequency (%)
95.70079824 1152
1.2%
97.07402499 1152
1.2%
114.8795876 1152
1.2%
114.8873204 1148
1.2%
121.6486851 1152
1.2%
126.0001272 1148
1.2%
129.7333877 1148
1.2%
134.845475 1590
1.7%
135.0678817 1590
1.7%
135.6663451 1590
1.7%
ValueCountFrequency (%)
197.9646832 5060
5.3%
191.9997559 5060
5.3%
190.6122798 5060
5.3%
184.0925037 5060
5.3%
180.7419731 5060
5.3%
174.7898983 5928
6.2%
167.4976716 5928
6.2%
166.1207282 1360
 
1.4%
165.5584764 5928
6.2%
161.9097208 1360
 
1.4%

GripRight
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)< 0.1%
Missing1152
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean163.90238
Minimum99.640817
Maximum193.41114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-10-30T20:30:11.721897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum99.640817
5-th percentile132.70929
Q1148.22592
median171.58622
Q3180.34178
95-th percentile193.41114
Maximum193.41114
Range93.770322
Interquartile range (IQR)32.115862

Descriptive statistics

Standard deviation21.814585
Coefficient of variation (CV)0.13309498
Kurtosis0.075799953
Mean163.90238
Median Absolute Deviation (MAD)17.106955
Skewness-0.80816385
Sum15388795
Variance475.8761
MonotonicityNot monotonic
2023-10-30T20:30:11.800507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
188.6931707 5928
 
6.2%
174.5382103 5928
 
6.2%
181.2299584 5928
 
6.2%
178.3647818 5928
 
6.2%
193.4111396 5060
 
5.3%
179.6923543 5060
 
5.3%
182.0643014 5060
 
5.3%
171.586216 5060
 
5.3%
180.3417831 5060
 
5.3%
179.8750722 2640
 
2.8%
Other values (33) 42238
44.4%
ValueCountFrequency (%)
99.64081708 1152
1.2%
106.6759325 1152
1.2%
110.3645614 1152
1.2%
111.8998226 1152
1.2%
132.7092867 1360
1.4%
133.0087555 990
1.0%
133.6933091 1148
1.2%
137.5115609 1148
1.2%
137.7262298 1360
1.4%
138.5682719 990
1.0%
ValueCountFrequency (%)
193.4111396 5060
5.3%
188.6931707 5928
6.2%
182.0643014 5060
5.3%
181.2299584 5928
6.2%
180.3417831 5060
5.3%
179.8750722 2640
2.8%
179.6923543 5060
5.3%
178.3647818 5928
6.2%
174.5382103 5928
6.2%
171.586216 5060
5.3%
Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
Minimum2023-02-18 00:00:00
Maximum2023-08-07 00:00:00
2023-10-30T20:30:11.878276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:11.955760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
Minimum2023-02-22 00:00:00
Maximum2023-07-22 00:00:00
2023-10-30T20:30:12.029815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:12.101877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)

HipExternalRotatationPassiveLeft
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)< 0.1%
Missing30685
Missing (%)32.3%
Infinite0
Infinite (%)0.0%
Mean33.307312
Minimum19.211221
Maximum48.57563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-10-30T20:30:12.167752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum19.211221
5-th percentile26.742781
Q126.926464
median29.744934
Q339.401758
95-th percentile48.57563
Maximum48.57563
Range29.364409
Interquartile range (IQR)12.475294

Descriptive statistics

Standard deviation7.3245622
Coefficient of variation (CV)0.21990854
Kurtosis-0.59460032
Mean33.307312
Median Absolute Deviation (MAD)3.0021526
Skewness0.61341683
Sum2143558.7
Variance53.649211
MonotonicityNot monotonic
2023-10-30T20:30:12.227664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
26.7427814 12650
13.3%
29.74493396 12650
13.3%
26.92646412 5760
 
6.1%
44.473823 3960
 
4.2%
32.23186006 3960
 
4.2%
39.40175838 3904
 
4.1%
48.57562966 3400
 
3.6%
45.40592121 3400
 
3.6%
32.14945443 3180
 
3.3%
35.26808674 3180
 
3.3%
Other values (5) 8313
 
8.7%
(Missing) 30685
32.3%
ValueCountFrequency (%)
19.21122051 1800
 
1.9%
26.7427814 12650
13.3%
26.92646412 5760
6.1%
29.74493396 12650
13.3%
32.14945443 3180
 
3.3%
32.23186006 3960
 
4.2%
35.26808674 3180
 
3.3%
35.70598711 1722
 
1.8%
36.00556801 1089
 
1.1%
36.50320689 1722
 
1.8%
ValueCountFrequency (%)
48.57562966 3400
3.6%
45.40592121 3400
3.6%
44.473823 3960
4.2%
42.55258653 1980
2.1%
39.40175838 3904
4.1%
36.50320689 1722
1.8%
36.00556801 1089
 
1.1%
35.70598711 1722
1.8%
35.26808674 3180
3.3%
32.23186006 3960
4.2%

HipExternalRotatationPassiveRight
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)< 0.1%
Missing30685
Missing (%)32.3%
Infinite0
Infinite (%)0.0%
Mean31.701933
Minimum21.950532
Maximum46.939179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-10-30T20:30:12.289936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum21.950532
5-th percentile21.950532
Q123.749192
median30.279283
Q338.480554
95-th percentile46.939179
Maximum46.939179
Range24.988647
Interquartile range (IQR)14.731363

Descriptive statistics

Standard deviation8.4623059
Coefficient of variation (CV)0.26693343
Kurtosis-1.297445
Mean31.701933
Median Absolute Deviation (MAD)6.5300912
Skewness0.31555944
Sum2040241.3
Variance71.610621
MonotonicityNot monotonic
2023-10-30T20:30:12.351302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
21.95053186 12650
13.3%
23.74919151 12650
13.3%
30.27928267 5760
 
6.1%
46.93917875 3960
 
4.2%
35.90838406 3960
 
4.2%
44.04357448 3904
 
4.1%
42.96689249 3400
 
3.6%
35.08814944 3400
 
3.6%
31.75676995 3180
 
3.3%
38.4805544 3180
 
3.3%
Other values (5) 8313
 
8.7%
(Missing) 30685
32.3%
ValueCountFrequency (%)
21.95053186 12650
13.3%
23.74919151 12650
13.3%
26.63816969 1800
 
1.9%
30.27928267 5760
6.1%
31.75676995 3180
 
3.3%
35.08814944 3400
 
3.6%
35.90838406 3960
 
4.2%
36.05033378 1722
 
1.8%
36.69870284 1089
 
1.1%
37.917355 1722
 
1.8%
ValueCountFrequency (%)
46.93917875 3960
4.2%
44.04357448 3904
4.1%
42.96689249 3400
3.6%
42.26307659 1980
2.1%
38.4805544 3180
3.3%
37.917355 1722
1.8%
36.69870284 1089
 
1.1%
36.05033378 1722
1.8%
35.90838406 3960
4.2%
35.08814944 3400
3.6%

ShoulderExternalRotatationStrengthLeft
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)< 0.1%
Missing30685
Missing (%)32.3%
Infinite0
Infinite (%)0.0%
Mean59.257242
Minimum33.863814
Maximum82.831239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-10-30T20:30:12.410320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum33.863814
5-th percentile35.15412
Q146.724666
median55.935014
Q371.16179
95-th percentile82.831239
Maximum82.831239
Range48.967425
Interquartile range (IQR)24.437123

Descriptive statistics

Standard deviation15.710405
Coefficient of variation (CV)0.26512211
Kurtosis-1.253504
Mean59.257242
Median Absolute Deviation (MAD)13.004091
Skewness0.22743033
Sum3813618.3
Variance246.81683
MonotonicityNot monotonic
2023-10-30T20:30:12.472014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
82.83123896 12650
13.3%
71.1617895 12650
13.3%
46.72466645 5760
 
6.1%
49.37743008 3960
 
4.2%
35.15412021 3960
 
4.2%
55.93501429 3904
 
4.1%
42.93092378 3400
 
3.6%
52.61526093 3400
 
3.6%
47.38317059 3180
 
3.3%
46.23423641 3180
 
3.3%
Other values (5) 8313
 
8.7%
(Missing) 30685
32.3%
ValueCountFrequency (%)
33.86381432 1800
 
1.9%
35.15412021 3960
4.2%
42.93092378 3400
3.6%
46.23423641 3180
3.3%
46.72466645 5760
6.1%
47.38317059 3180
3.3%
49.37743008 3960
4.2%
49.39494812 1722
 
1.8%
52.61526093 3400
3.6%
53.68671612 1722
 
1.8%
ValueCountFrequency (%)
82.83123896 12650
13.3%
71.1617895 12650
13.3%
59.79638267 1980
 
2.1%
58.71304702 1089
 
1.1%
55.93501429 3904
 
4.1%
53.68671612 1722
 
1.8%
52.61526093 3400
 
3.6%
49.39494812 1722
 
1.8%
49.37743008 3960
 
4.2%
47.38317059 3180
 
3.3%

ShoulderExternalRotatationStrengthRight
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)< 0.1%
Missing30685
Missing (%)32.3%
Infinite0
Infinite (%)0.0%
Mean56.926577
Minimum28.234395
Maximum78.099105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-10-30T20:30:12.542564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum28.234395
5-th percentile38.519851
Q143.989698
median52.408248
Q370.561127
95-th percentile78.099105
Maximum78.099105
Range49.86471
Interquartile range (IQR)26.571429

Descriptive statistics

Standard deviation15.741899
Coefficient of variation (CV)0.27652986
Kurtosis-1.5525101
Mean56.926577
Median Absolute Deviation (MAD)13.151625
Skewness0.070286823
Sum3663623.7
Variance247.80737
MonotonicityNot monotonic
2023-10-30T20:30:12.618353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
70.56112681 12650
13.3%
78.09910515 12650
13.3%
39.90689977 5760
 
6.1%
48.42637889 3960
 
4.2%
44.25702842 3960
 
4.2%
65.55987267 3904
 
4.1%
38.51985082 3400
 
3.6%
41.45303925 3400
 
3.6%
44.33276079 3180
 
3.3%
43.98969826 3180
 
3.3%
Other values (5) 8313
 
8.7%
(Missing) 30685
32.3%
ValueCountFrequency (%)
28.23439517 1800
 
1.9%
38.51985082 3400
3.6%
39.90689977 5760
6.1%
40.12007384 1722
 
1.8%
41.45303925 3400
3.6%
43.98969826 3180
3.3%
44.25702842 3960
4.2%
44.33276079 3180
3.3%
48.42637889 3960
4.2%
51.0308892 1089
 
1.1%
ValueCountFrequency (%)
78.09910515 12650
13.3%
70.56112681 12650
13.3%
65.55987267 3904
 
4.1%
56.43154134 1980
 
2.1%
52.40824807 1722
 
1.8%
51.0308892 1089
 
1.1%
48.42637889 3960
 
4.2%
44.33276079 3180
 
3.3%
44.25702842 3960
 
4.2%
43.98969826 3180
 
3.3%

ASLRCoreStabilityLeft
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing42934
Missing (%)45.2%
Memory size3.5 MiB
1.0
52108 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters156324
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 52108
54.8%
(Missing) 42934
45.2%

Length

2023-10-30T20:30:12.693653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-30T20:30:12.754584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 52108
100.0%

Most occurring characters

ValueCountFrequency (%)
1 52108
33.3%
. 52108
33.3%
0 52108
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 104216
66.7%
Other Punctuation 52108
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 52108
50.0%
0 52108
50.0%
Other Punctuation
ValueCountFrequency (%)
. 52108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 156324
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 52108
33.3%
. 52108
33.3%
0 52108
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 156324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 52108
33.3%
. 52108
33.3%
0 52108
33.3%

ASLRCoreStabilityRight
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing42934
Missing (%)45.2%
Memory size3.5 MiB
1.0
39458 
0.0
12650 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters156324
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 39458
41.5%
0.0 12650
 
13.3%
(Missing) 42934
45.2%

Length

2023-10-30T20:30:12.803914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-30T20:30:12.865753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 39458
75.7%
0.0 12650
 
24.3%

Most occurring characters

ValueCountFrequency (%)
0 64758
41.4%
. 52108
33.3%
1 39458
25.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 104216
66.7%
Other Punctuation 52108
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 64758
62.1%
1 39458
37.9%
Other Punctuation
ValueCountFrequency (%)
. 52108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 156324
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 64758
41.4%
. 52108
33.3%
1 39458
25.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 156324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 64758
41.4%
. 52108
33.3%
1 39458
25.2%

AvgBrakingForceNewtons
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct130
Distinct (%)0.2%
Missing20478
Missing (%)21.5%
Infinite0
Infinite (%)0.0%
Mean1859.3293
Minimum1246.3573
Maximum2524.9609
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-10-30T20:30:12.932031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1246.3573
5-th percentile1549.934
Q11663.7417
median1805.4483
Q32046.0752
95-th percentile2298.4395
Maximum2524.9609
Range1278.6036
Interquartile range (IQR)382.33343

Descriptive statistics

Standard deviation242.49123
Coefficient of variation (CV)0.13041866
Kurtosis-0.27596567
Mean1859.3293
Median Absolute Deviation (MAD)164.9916
Skewness0.31034856
Sum1.3863903 × 108
Variance58801.995
MonotonicityNot monotonic
2023-10-30T20:30:13.017915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1640.456664 1824
 
1.9%
1663.741749 1824
 
1.9%
1669.076167 1824
 
1.9%
1815.102011 1824
 
1.9%
1885.076167 1824
 
1.9%
1764.552974 1824
 
1.9%
1780.447816 1824
 
1.9%
1640.079493 1824
 
1.9%
1671.552974 1824
 
1.9%
1653.759261 1824
 
1.9%
Other values (120) 56324
59.3%
(Missing) 20478
 
21.5%
ValueCountFrequency (%)
1246.357288 360
0.4%
1317.530468 360
0.4%
1338.577029 360
0.4%
1364.405849 360
0.4%
1391.366168 360
0.4%
1411.366168 360
0.4%
1424.70888 360
0.4%
1439.404515 360
0.4%
1455.192678 60
 
0.1%
1499.653995 360
0.4%
ValueCountFrequency (%)
2524.960878 424
0.4%
2486.070448 198
0.2%
2439.732529 198
0.2%
2427.313362 424
0.4%
2396.919535 424
0.4%
2374.919535 424
0.4%
2372.3988 198
0.2%
2354.666825 198
0.2%
2322.86701 198
0.2%
2315.686846 424
0.4%

PeakPropulsiveForceNewtons
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct172
Distinct (%)0.2%
Missing11478
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean2495.7617
Minimum1778.3916
Maximum3309.4362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-10-30T20:30:13.101502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1778.3916
5-th percentile2069.1009
Q12174.1926
median2581.6806
Q32799.8992
95-th percentile2958.1873
Maximum3309.4362
Range1531.0446
Interquartile range (IQR)625.7066

Descriptive statistics

Standard deviation330.62493
Coefficient of variation (CV)0.13247456
Kurtosis-1.4365199
Mean2495.7617
Median Absolute Deviation (MAD)322.05425
Skewness0.067527568
Sum2.0855583 × 108
Variance109312.84
MonotonicityNot monotonic
2023-10-30T20:30:13.187054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2299.192627 1824
 
1.9%
2361.022443 1824
 
1.9%
2249.439683 1824
 
1.9%
2125.022443 1824
 
1.9%
2140.739837 1824
 
1.9%
2082.717415 1824
 
1.9%
2156.011268 1824
 
1.9%
2267.66125 1824
 
1.9%
2180.195524 1824
 
1.9%
2174.192627 1824
 
1.9%
Other values (162) 65324
68.7%
(Missing) 11478
 
12.1%
ValueCountFrequency (%)
1778.391603 200
0.2%
1803.944216 60
 
0.1%
1815.699281 60
 
0.1%
1885.699281 60
 
0.1%
1924.920541 60
 
0.1%
1938.61135 60
 
0.1%
1960.510835 60
 
0.1%
1965.510835 60
 
0.1%
1968.61135 60
 
0.1%
1973.76255 60
 
0.1%
ValueCountFrequency (%)
3309.436203 198
0.2%
3163.98619 424
0.4%
3156.16993 198
0.2%
3107.886434 424
0.4%
3024.643833 264
0.3%
3022.088376 198
0.2%
3019.584483 320
0.3%
3012.642092 424
0.4%
2996.281702 198
0.2%
2991.742426 264
0.3%

JumpHeightMeters
Real number (ℝ)

MISSING 

Distinct165
Distinct (%)0.2%
Missing11478
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean0.44367343
Minimum-0.57388443
Maximum1.5018376
Zeros0
Zeros (%)0.0%
Negative21110
Negative (%)22.2%
Memory size3.5 MiB
2023-10-30T20:30:13.280580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.57388443
5-th percentile-0.43504709
Q1-0.011190428
median0.50864976
Q30.96172098
95-th percentile1.3088699
Maximum1.5018376
Range2.075722
Interquartile range (IQR)0.97291141

Descriptive statistics

Standard deviation0.55239114
Coefficient of variation (CV)1.2450399
Kurtosis-1.1632391
Mean0.44367343
Median Absolute Deviation (MAD)0.48620012
Skewness-0.085013221
Sum37075.126
Variance0.30513597
MonotonicityNot monotonic
2023-10-30T20:30:13.361646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1929044486 1824
 
1.9%
0.631775951 1824
 
1.9%
0.4040791309 1824
 
1.9%
0.8002679371 1824
 
1.9%
0.5134214582 1824
 
1.9%
0.1507048728 1824
 
1.9%
0.5434214594 1824
 
1.9%
0.2129044295 1824
 
1.9%
-0.06254967861 1824
 
1.9%
0.6517759617 1824
 
1.9%
Other values (155) 65324
68.7%
(Missing) 11478
 
12.1%
ValueCountFrequency (%)
-0.5738844294 200
 
0.2%
-0.5698055667 200
 
0.2%
-0.5561567373 60
 
0.1%
-0.5481249657 976
1.0%
-0.5381249753 976
1.0%
-0.4978102548 200
 
0.2%
-0.4961567647 60
 
0.1%
-0.4960672287 424
0.4%
-0.4898055834 200
 
0.2%
-0.4638844151 200
 
0.2%
ValueCountFrequency (%)
1.501837559 264
 
0.3%
1.423734469 60
 
0.1%
1.388749295 60
 
0.1%
1.383734477 60
 
0.1%
1.361000929 720
0.8%
1.359161709 424
0.4%
1.350455693 424
0.4%
1.341000918 360
0.4%
1.338749283 60
 
0.1%
1.329161708 424
0.4%

ImpulseRatio
Real number (ℝ)

MISSING 

Distinct130
Distinct (%)0.2%
Missing20478
Missing (%)21.5%
Infinite0
Infinite (%)0.0%
Mean1.7815401
Minimum0.61265509
Maximum3.6775035
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-10-30T20:30:13.447498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.61265509
5-th percentile0.9316929
Q11.203943
median1.6885433
Q32.2921179
95-th percentile2.8442827
Maximum3.6775035
Range3.0648484
Interquartile range (IQR)1.0881749

Descriptive statistics

Standard deviation0.65580595
Coefficient of variation (CV)0.3681118
Kurtosis-0.7526211
Mean1.7815401
Median Absolute Deviation (MAD)0.50514137
Skewness0.35929075
Sum132838.76
Variance0.43008144
MonotonicityNot monotonic
2023-10-30T20:30:13.529918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.527702759 1824
 
1.9%
1.041263978 1824
 
1.9%
0.9538432034 1824
 
1.9%
1.582007141 1824
 
1.9%
0.9438432129 1824
 
1.9%
1.78079376 1824
 
1.9%
1.184780143 1824
 
1.9%
1.119939064 1824
 
1.9%
1.830793712 1824
 
1.9%
1.203942987 1824
 
1.9%
Other values (120) 56324
59.3%
(Missing) 20478
 
21.5%
ValueCountFrequency (%)
0.6126550897 976
1.0%
0.662655042 976
1.0%
0.7714899569 492
 
0.5%
0.8414898901 492
 
0.5%
0.8914899617 492
 
0.5%
0.9316929043 976
1.0%
0.9400415127 60
 
0.1%
0.9438432129 1824
1.9%
0.9538432034 1824
1.9%
0.9866809426 424
 
0.4%
ValueCountFrequency (%)
3.677503527 320
 
0.3%
3.206979909 320
 
0.3%
3.178126989 320
 
0.3%
3.099058322 1150
1.2%
3.072815571 320
 
0.3%
3.070849588 360
 
0.4%
2.988979304 200
 
0.2%
2.984684072 264
 
0.3%
2.961350359 60
 
0.1%
2.957687494 60
 
0.1%

LeftAvgBrakingForceNewtons
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct130
Distinct (%)0.2%
Missing20478
Missing (%)21.5%
Infinite0
Infinite (%)0.0%
Mean939.70744
Minimum535.62593
Maximum1202.2957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-10-30T20:30:13.617893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum535.62593
5-th percentile711.08929
Q1833.75095
median938.03309
Q31048.715
95-th percentile1161.3306
Maximum1202.2957
Range666.66978
Interquartile range (IQR)214.96407

Descriptive statistics

Standard deviation136.70439
Coefficient of variation (CV)0.14547548
Kurtosis-0.1319939
Mean939.70744
Median Absolute Deviation (MAD)105.70563
Skewness-0.24408155
Sum70068346
Variance18688.09
MonotonicityNot monotonic
2023-10-30T20:30:13.702753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
818.6026324 1824
 
1.9%
850.4880791 1824
 
1.9%
847.9508109 1824
 
1.9%
907.2959226 1824
 
1.9%
935.9508109 1824
 
1.9%
908.4307434 1824
 
1.9%
871.8521483 1824
 
1.9%
809.1263571 1824
 
1.9%
869.4307434 1824
 
1.9%
833.7509513 1824
 
1.9%
Other values (120) 56324
59.3%
(Missing) 20478
 
21.5%
ValueCountFrequency (%)
535.6259336 360
0.4%
553.1333435 360
0.4%
583.4204213 360
0.4%
590.5360542 360
0.4%
624.889411 360
0.4%
638.889411 360
0.4%
639.6772698 360
0.4%
655.8162739 360
0.4%
672.7242023 360
0.4%
704.4690554 360
0.4%
ValueCountFrequency (%)
1202.295713 424
 
0.4%
1190.134116 1150
1.2%
1180.591517 424
 
0.4%
1179.294828 424
 
0.4%
1163.202141 1150
1.2%
1161.330612 264
 
0.3%
1158.212109 1150
1.2%
1151.095628 1150
1.2%
1139.061592 424
 
0.4%
1137.684776 198
 
0.2%

RightAvgBrakingForceNewtons
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct130
Distinct (%)0.2%
Missing20478
Missing (%)21.5%
Infinite0
Infinite (%)0.0%
Mean919.5034
Minimum711.05219
Maximum1407.8718
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2023-10-30T20:30:13.791506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum711.05219
5-th percentile757.43518
Q1814.42438
median872.8184
Q31000.5931
95-th percentile1216.1234
Maximum1407.8718
Range696.81957
Interquartile range (IQR)186.16869

Descriptive statistics

Standard deviation140.13608
Coefficient of variation (CV)0.15240409
Kurtosis0.39514356
Mean919.5034
Median Absolute Deviation (MAD)66.024921
Skewness1.1076394
Sum68561851
Variance19638.12
MonotonicityNot monotonic
2023-10-30T20:30:13.874623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
821.2747721 1824
 
1.9%
813.6131599 1824
 
1.9%
820.0484415 1824
 
1.9%
907.6872131 1824
 
1.9%
948.0484415 1824
 
1.9%
855.0107081 1824
 
1.9%
907.6786651 1824
 
1.9%
832.5010521 1824
 
1.9%
802.0107081 1824
 
1.9%
819.0144422 1824
 
1.9%
Other values (120) 56324
59.3%
(Missing) 20478
 
21.5%
ValueCountFrequency (%)
711.0521942 360
 
0.4%
743.28652 60
 
0.1%
748.8159726 360
 
0.4%
750.4657009 1150
1.2%
757.1235508 60
 
0.1%
757.4351811 1824
1.9%
765.1618007 360
 
0.4%
767.0523347 360
 
0.4%
773.0523347 360
 
0.4%
779.8696772 360
 
0.4%
ValueCountFrequency (%)
1407.871766 198
0.2%
1322.285989 424
0.4%
1316.153946 198
0.2%
1311.881799 424
0.4%
1284.911644 198
0.2%
1278.949021 198
0.2%
1275.036721 198
0.2%
1246.75584 424
0.4%
1235.881799 424
0.4%
1224.401689 424
0.4%
Distinct95
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
Minimum2023-02-15 00:00:00
Maximum2023-07-31 00:00:00
2023-10-30T20:30:13.964697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:14.048965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-10-30T20:30:04.793591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:27.595330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:29.357785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:31.978125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:33.920856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:35.725297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:37.804043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:39.526510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:41.493953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:43.221325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:45.010870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:46.944642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:48.625809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:50.351386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:52.033315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:53.913472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:55.615476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:57.385074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:59.222201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:01.222451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:02.969462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:04.875427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:27.681078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:29.435291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:32.059033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:34.001255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:35.805447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:37.882513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:39.608271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:41.571827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:43.303539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:45.088316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:47.022372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:48.710248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:50.430102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:52.112096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:53.994516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:55.704191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:57.468866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:59.296845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:01.302193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:03.049121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:04.959203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:27.762174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:30.381234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:32.143464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:34.084065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:35.904387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:37.964905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:39.693599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:41.654466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:43.387266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:45.168184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:47.100380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:48.791236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:50.507990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:52.190810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:54.072034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:55.789954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:57.559660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:59.376143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:01.382946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:03.132658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:05.057534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:27.846510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:30.468385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:32.234629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:34.175245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:36.057569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:38.051328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:39.785757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:41.740819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:43.476316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:45.256884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:47.185044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:48.875904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:50.591803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:52.273318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:54.155952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:55.880177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:57.654383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:59.462653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:01.470188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:03.222018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:05.153012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:27.934752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:30.555152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:32.325061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:34.262324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:36.212158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:38.137655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:39.876781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:41.824047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:43.564533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:45.342748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:47.268251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:48.959897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:50.675405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:52.553329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:54.238834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:55.966453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:57.742490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:59.545502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:01.557125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:03.310834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:05.249204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:28.017391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:30.643596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:32.426088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:34.348756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:36.310214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:38.220962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-10-30T20:30:02.372864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:04.147833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:06.090354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:28.849833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:31.464978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:33.393298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:35.201797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:37.270544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:39.028973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:40.812047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:42.724570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:44.490525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:46.446188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:48.143888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:49.854357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:51.552168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:53.426329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:55.118633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:56.865718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:58.699876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:00.720447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:02.456760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:04.255830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:06.179456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:28.940959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:31.551206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:33.480000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:35.287289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:37.354727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:39.111185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:41.052991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:42.808837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:44.575464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:46.532071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:48.224136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:49.942033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:51.636282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:53.507776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:55.198446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:56.952043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:58.787112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:00.805566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:02.543327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:04.351473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:06.266670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:29.032782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:31.637260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:33.570648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:35.374103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:37.441814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:39.196608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:41.142980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:42.893521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:44.666083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:46.617535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:48.307243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:50.025761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:51.717786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:53.591209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:55.287123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:57.038310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:58.876086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:00.890528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:02.629743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:04.445680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:06.351581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:29.107730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:31.714280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:33.653059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:35.454926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:37.526237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:39.274410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:41.225108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:42.971944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:44.746771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:46.696283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:48.380591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:50.102434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:51.792491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:53.668375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:55.364446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:57.119527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:58.958342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:00.967776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:02.710345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:04.527491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:06.437535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:29.188284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:31.800681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:33.743154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:35.543253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:37.623533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:39.356232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:41.315008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:43.051974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:44.831922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:46.775576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:48.460912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:50.183009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:51.870401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:53.746689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:55.444494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:57.206158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:59.043745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:01.047569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:02.793717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:04.613439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:06.524858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:29.270536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:31.885446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:33.829030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:35.630216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:37.710300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:39.437282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:41.401355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:43.134524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:44.918862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:46.857651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:48.542164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:50.264383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:51.948894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:53.827581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:55.524441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:57.292485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:29:59.130692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:01.135597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:02.878252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-30T20:30:04.700189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-10-30T20:30:14.155936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
PitchIdVelocityHorizontalBreakInducedVerticalBreakKneeStrideFlexionFCKneeStrideFlexionAngVeloPeakShoulderThrowRotationMERElbowThrowFlexionFCHipChestSeparationPeakGripLeftGripRightHipExternalRotatationPassiveLeftHipExternalRotatationPassiveRightShoulderExternalRotatationStrengthLeftShoulderExternalRotatationStrengthRightAvgBrakingForceNewtonsPeakPropulsiveForceNewtonsJumpHeightMetersImpulseRatioLeftAvgBrakingForceNewtonsRightAvgBrakingForceNewtonsPlayerCodePitchTypeCombinedDiagnosisASLRCoreStabilityRight
PitchId1.000-0.104-0.031-0.130-0.243-0.3400.029-0.177-0.5450.2910.456-0.306-0.3190.4290.477-0.290-0.219-0.066-0.270-0.227-0.2890.3190.1510.3600.400
Velocity-0.1041.000-0.5670.8250.2570.251-0.0480.1410.2830.021-0.006-0.115-0.089-0.024-0.012-0.0220.0220.0270.0840.011-0.0460.2480.4900.2440.461
HorizontalBreak-0.031-0.5671.000-0.4530.0770.1570.1510.0510.0630.1130.0410.086-0.0370.1290.1610.1670.063-0.0160.0530.2050.0830.2630.4650.2520.519
InducedVerticalBreak-0.1300.825-0.4531.0000.1800.1160.0400.1260.1810.0720.054-0.156-0.114-0.013-0.002-0.035-0.0130.0190.0820.003-0.0710.2610.5520.2360.390
KneeStrideFlexionFC-0.2430.2570.0770.1801.0000.3880.4320.2630.2230.015-0.039-0.503-0.3960.1630.2070.4820.5770.1320.2640.5380.2930.4800.1970.3630.507
KneeStrideFlexionAngVeloPeak-0.3400.2510.1570.1160.3881.0000.285-0.0330.496-0.007-0.2220.1850.1090.1530.3210.3560.2120.0760.2170.3640.2400.4540.2290.4870.713
ShoulderThrowRotationMER0.029-0.0480.1510.0400.4320.2851.000-0.288-0.1840.3970.248-0.709-0.7140.6700.6680.2150.4020.1650.2630.381-0.0510.0790.0500.0540.081
ElbowThrowFlexionFC-0.1770.1410.0510.1260.263-0.033-0.2881.0000.156-0.215-0.2390.2840.176-0.147-0.2830.4300.157-0.102-0.1870.2210.5390.5090.2480.5280.198
HipChestSeparationPeak-0.5450.2830.0630.1810.2230.496-0.1840.1561.000-0.213-0.4230.3490.282-0.379-0.4350.2090.1600.0350.2900.2060.1870.4650.2070.4650.403
GripLeft0.2910.0210.1130.0720.015-0.0070.397-0.215-0.2131.0000.766-0.407-0.6590.7180.722-0.192-0.129-0.1130.1380.119-0.4490.6300.2430.4981.000
GripRight0.456-0.0060.0410.054-0.039-0.2220.248-0.239-0.4230.7661.000-0.491-0.6320.6520.752-0.243-0.166-0.090-0.0280.000-0.4320.5690.2130.4070.359
HipExternalRotatationPassiveLeft-0.306-0.1150.086-0.156-0.5030.185-0.7090.2840.349-0.407-0.4911.0000.877-0.478-0.3600.114-0.354-0.086-0.308-0.2720.4020.8250.2910.7831.000
HipExternalRotatationPassiveRight-0.319-0.089-0.037-0.114-0.3960.109-0.7140.1760.282-0.659-0.6320.8771.000-0.694-0.5590.094-0.3140.004-0.323-0.3710.4260.8360.2580.6971.000
ShoulderExternalRotatationStrengthLeft0.429-0.0240.129-0.0130.1630.1530.670-0.147-0.3790.7180.652-0.478-0.6941.0000.8380.1800.341-0.0410.0670.490-0.1620.8020.3050.7361.000
ShoulderExternalRotatationStrengthRight0.477-0.0120.161-0.0020.2070.3210.668-0.283-0.4350.7220.752-0.360-0.5590.8381.0000.1940.262-0.0230.0220.494-0.1800.8720.3020.7201.000
AvgBrakingForceNewtons-0.290-0.0220.167-0.0350.4820.3560.2150.4300.209-0.192-0.2430.1140.0940.1800.1941.0000.8260.057-0.0430.8930.8590.5180.1810.5470.463
PeakPropulsiveForceNewtons-0.2190.0220.063-0.0130.5770.2120.4020.1570.160-0.129-0.166-0.354-0.3140.3410.2620.8261.0000.1470.2210.8270.6370.4440.1640.4860.538
JumpHeightMeters-0.0660.027-0.0160.0190.1320.0760.165-0.1020.035-0.113-0.090-0.0860.004-0.041-0.0230.0570.1471.000-0.0050.0700.0260.3360.1460.3250.312
ImpulseRatio-0.2700.0840.0530.0820.2640.2170.263-0.1870.2900.138-0.028-0.308-0.3230.0670.022-0.0430.221-0.0051.0000.098-0.2000.3540.1680.3840.494
LeftAvgBrakingForceNewtons-0.2270.0110.2050.0030.5380.3640.3810.2210.2060.1190.000-0.272-0.3710.4900.4940.8930.8270.0700.0981.0000.5760.5070.1790.5750.725
RightAvgBrakingForceNewtons-0.289-0.0460.083-0.0710.2930.240-0.0510.5390.187-0.449-0.4320.4020.426-0.162-0.1800.8590.6370.026-0.2000.5761.0000.4460.2020.4490.246
PlayerCode0.3190.2480.2630.2610.4800.4540.0790.5090.4650.6300.5690.8250.8360.8020.8720.5180.4440.3360.3540.5070.4461.0000.3731.0001.000
PitchType0.1510.4900.4650.5520.1970.2290.0500.2480.2070.2430.2130.2910.2580.3050.3020.1810.1640.1460.1680.1790.2020.3731.0000.3210.477
CombinedDiagnosis0.3600.2440.2520.2360.3630.4870.0540.5280.4650.4980.4070.7830.6970.7360.7200.5470.4860.3250.3840.5750.4491.0000.3211.0000.766
ASLRCoreStabilityRight0.4000.4610.5190.3900.5070.7130.0810.1980.4031.0000.3591.0001.0001.0001.0000.4630.5380.3120.4940.7250.2461.0000.4770.7661.000

Missing values

2023-10-30T20:30:06.706914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-30T20:30:07.327510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-30T20:30:07.926709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PlayerCodePitchIdPitchDatePitchTimeThrowHandPitchTypeVelocityHorizontalBreakInducedVerticalBreakKneeStrideFlexionFCKneeStrideFlexionAngVeloPeakShoulderThrowRotationMERElbowThrowFlexionFCHipChestSeparationPeakInjuredDateCombinedDiagnosisGripLeftGripRightGripDateAssessedPerformanceDateAssessedHipExternalRotatationPassiveLeftHipExternalRotatationPassiveRightShoulderExternalRotatationStrengthLeftShoulderExternalRotatationStrengthRightASLRCoreStabilityLeftASLRCoreStabilityRightAvgBrakingForceNewtonsPeakPropulsiveForceNewtonsJumpHeightMetersImpulseRatioLeftAvgBrakingForceNewtonsRightAvgBrakingForceNewtonsJumpDateAssessed
0VR1T212023-04-0823:25:11RCB75.26355610.061752-8.95616142.692695425.256171-186.76716670.56571245.8969552023-04-08R Arm/Elbow Tendinitis149.962333152.6840552023-03-192023-02-2444.47382346.93917949.3774348.426379NaNNaNNaN2159.4853531.361001NaNNaNNaN2023-03-25
1VR1T212023-04-0823:25:11RCB75.26355610.061752-8.95616142.692695425.256171-186.76716670.56571245.8969552023-04-08R Arm/Elbow Tendinitis149.962333152.6840552023-03-192023-02-2444.47382346.93917949.3774348.426379NaNNaNNaN2142.2862751.085226NaNNaNNaN2023-04-02
2VR1T212023-04-0823:25:11RCB75.26355610.061752-8.95616142.692695425.256171-186.76716670.56571245.8969552023-04-08R Arm/Elbow Tendinitis149.962333152.6840552023-03-192023-02-2444.47382346.93917949.3774348.426379NaNNaN1439.4045152117.1224031.0589901.817174655.816274784.0511922023-03-23
3VR1T212023-04-0823:25:11RCB75.26355610.061752-8.95616142.692695425.256171-186.76716670.56571245.8969552023-04-08R Arm/Elbow Tendinitis149.962333152.6840552023-03-192023-02-2444.47382346.93917949.3774348.426379NaNNaNNaN2148.2862751.075226NaNNaNNaN2023-04-02
4VR1T212023-04-0823:25:11RCB75.26355610.061752-8.95616142.692695425.256171-186.76716670.56571245.8969552023-04-08R Arm/Elbow Tendinitis149.962333152.6840552023-03-192023-02-2444.47382346.93917949.3774348.426379NaNNaN1499.6539952045.246208-0.2990021.934612672.724202825.6935742023-03-16
5VR1T212023-04-0823:25:11RCB75.26355610.061752-8.95616142.692695425.256171-186.76716670.56571245.8969552023-04-08R Arm/Elbow Tendinitis149.962333152.6840552023-03-192023-02-2444.47382346.93917949.3774348.426379NaNNaNNaN2203.5606230.829442NaNNaNNaN2023-04-06
6VR1T212023-04-0823:25:11RCB75.26355610.061752-8.95616142.692695425.256171-186.76716670.56571245.8969552023-04-08R Arm/Elbow Tendinitis149.962333152.6840552023-03-192023-02-2444.47382346.93917949.3774348.426379NaNNaN1364.4058492089.3387520.9884752.282544583.420421779.8696772023-04-07
7VR1T212023-04-0823:25:11RCB75.26355610.061752-8.95616142.692695425.256171-186.76716670.56571245.8969552023-04-08R Arm/Elbow Tendinitis149.962333152.6840552023-03-192023-02-2444.47382346.93917949.3774348.426379NaNNaN1317.5304682101.5570821.0222902.125044553.133343765.1618012023-04-01
9VR1T212023-04-0823:25:11RCB75.26355610.061752-8.95616142.692695425.256171-186.76716670.56571245.8969552023-04-08R Arm/Elbow Tendinitis149.962333152.6840552023-03-192023-02-2444.47382346.93917949.3774348.426379NaNNaN1411.3661682052.378833-0.4100462.483085638.889411773.0523352023-03-26
10VR1T212023-04-0823:25:11RCB75.26355610.061752-8.95616142.692695425.256171-186.76716670.56571245.8969552023-04-08R Arm/Elbow Tendinitis149.962333152.6840552023-03-192023-02-2444.47382346.93917949.3774348.426379NaNNaNNaN2194.1733390.886754NaNNaNNaN2023-04-08
PlayerCodePitchIdPitchDatePitchTimeThrowHandPitchTypeVelocityHorizontalBreakInducedVerticalBreakKneeStrideFlexionFCKneeStrideFlexionAngVeloPeakShoulderThrowRotationMERElbowThrowFlexionFCHipChestSeparationPeakInjuredDateCombinedDiagnosisGripLeftGripRightGripDateAssessedPerformanceDateAssessedHipExternalRotatationPassiveLeftHipExternalRotatationPassiveRightShoulderExternalRotatationStrengthLeftShoulderExternalRotatationStrengthRightASLRCoreStabilityLeftASLRCoreStabilityRightAvgBrakingForceNewtonsPeakPropulsiveForceNewtonsJumpHeightMetersImpulseRatioLeftAvgBrakingForceNewtonsRightAvgBrakingForceNewtonsJumpDateAssessed
96728KYOF1142023-06-1317:30:15RSL85.3905454.118311.61194149.906738434.032176-174.54552196.30086448.1675822023-06-19R Arm/Elbow Strain180.741973193.411142023-06-182023-05-1829.74493423.74919271.1617978.0991051.00.0NaNNaNNaNNaNNaNNaN2023-06-05
96729KYOF1142023-06-1317:30:15RSL85.3905454.118311.61194149.906738434.032176-174.54552196.30086448.1675822023-06-19R Arm/Elbow Strain180.741973193.411142023-06-182023-05-1829.74493423.74919271.1617978.0991051.00.01777.0396102723.8696400.6456481.547898939.583941838.4923682023-06-08
96730KYOF1142023-06-1317:30:15RSL85.3905454.118311.61194149.906738434.032176-174.54552196.30086448.1675822023-06-19R Arm/Elbow Strain180.741973193.411142023-06-182023-05-1829.74493423.74919271.1617978.0991051.00.0NaNNaNNaNNaNNaNNaN2023-06-08
96731KYOF1142023-06-1317:30:15RSL85.3905454.118311.61194149.906738434.032176-174.54552196.30086448.1675822023-06-19R Arm/Elbow Strain180.741973193.411142023-06-182023-05-1829.74493423.74919271.1617978.0991051.00.02032.4689932910.3683920.1449572.6591711151.095628880.9608752023-05-21
96732KYOF1142023-06-1317:30:15RSL85.3905454.118311.61194149.906738434.032176-174.54552196.30086448.1675822023-06-19R Arm/Elbow Strain180.741973193.411142023-06-182023-05-1829.74493423.74919271.1617978.0991051.00.01755.3116162649.9876320.8874972.1089821006.457676750.4657012023-05-29
96733KYOF1142023-06-1317:30:15RSL85.3905454.118311.61194149.906738434.032176-174.54552196.30086448.1675822023-06-19R Arm/Elbow Strain180.741973193.411142023-06-182023-05-1829.74493423.74919271.1617978.0991051.00.0NaNNaNNaNNaNNaNNaN2023-06-06
96734KYOF1142023-06-1317:30:15RSL85.3905454.118311.61194149.906738434.032176-174.54552196.30086448.1675822023-06-19R Arm/Elbow Strain180.741973193.411142023-06-182023-05-1829.74493423.74919271.1617978.0991051.00.01884.2910692681.4249750.0051692.4491461053.100362830.0622202023-06-03
96735KYOF1142023-06-1317:30:15RSL85.3905454.118311.61194149.906738434.032176-174.54552196.30086448.1675822023-06-19R Arm/Elbow Strain180.741973193.411142023-06-182023-05-1829.74493423.74919271.1617978.0991051.00.01916.7819552734.6133081.0267192.2921181088.755632827.9448122023-05-22
96736KYOF1142023-06-1317:30:15RSL85.3905454.118311.61194149.906738434.032176-174.54552196.30086448.1675822023-06-19R Arm/Elbow Strain180.741973193.411142023-06-182023-05-1829.74493423.74919271.1617978.0991051.00.01937.7166762708.5851291.0476611.2285051070.196536867.1628512023-05-26
96737KYOF1142023-06-1317:30:15RSL85.3905454.118311.61194149.906738434.032176-174.54552196.30086448.1675822023-06-19R Arm/Elbow Strain180.741973193.411142023-06-182023-05-1829.74493423.74919271.1617978.0991051.00.01766.9808062616.4784010.2078351.965835952.444974813.8749042023-06-18

Duplicate rows

Most frequently occurring

PlayerCodePitchIdPitchDatePitchTimeThrowHandPitchTypeVelocityHorizontalBreakInducedVerticalBreakKneeStrideFlexionFCKneeStrideFlexionAngVeloPeakShoulderThrowRotationMERElbowThrowFlexionFCHipChestSeparationPeakInjuredDateCombinedDiagnosisGripLeftGripRightGripDateAssessedPerformanceDateAssessedHipExternalRotatationPassiveLeftHipExternalRotatationPassiveRightShoulderExternalRotatationStrengthLeftShoulderExternalRotatationStrengthRightASLRCoreStabilityLeftASLRCoreStabilityRightAvgBrakingForceNewtonsPeakPropulsiveForceNewtonsJumpHeightMetersImpulseRatioLeftAvgBrakingForceNewtonsRightAvgBrakingForceNewtonsJumpDateAssessed# duplicates
063FZ12023-05-2712:35:14RFB88.585878-7.10670113.94038449.317333505.493066-195.852250102.26693772.9764422023-06-23R Arm/Elbow Strain140.789087133.0087552023-05-312023-05-1742.55258742.26307759.79638356.431541NaNNaNNaNNaNNaNNaNNaNNaN2023-06-062
163FZ12023-05-2712:35:14RFB88.585878-7.10670113.94038449.317333505.493066-195.852250102.26693772.9764422023-06-23R Arm/Elbow Strain140.789087133.0087552023-05-312023-06-17NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2023-06-062
263FZ12023-05-2712:35:14RFB88.585878-7.10670113.94038449.317333505.493066-195.852250102.26693772.9764422023-06-23R Arm/Elbow Strain146.641119149.6979442023-06-092023-05-1742.55258742.26307759.79638356.431541NaNNaNNaNNaNNaNNaNNaNNaN2023-06-062
363FZ12023-05-2712:35:14RFB88.585878-7.10670113.94038449.317333505.493066-195.852250102.26693772.9764422023-06-23R Arm/Elbow Strain146.641119149.6979442023-06-092023-06-17NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2023-06-062
463FZ12023-05-2712:35:14RFB88.585878-7.10670113.94038449.317333505.493066-195.852250102.26693772.9764422023-06-23R Arm/Elbow Strain146.699601138.5682722023-05-262023-05-1742.55258742.26307759.79638356.431541NaNNaNNaNNaNNaNNaNNaNNaN2023-06-062
563FZ12023-05-2712:35:14RFB88.585878-7.10670113.94038449.317333505.493066-195.852250102.26693772.9764422023-06-23R Arm/Elbow Strain146.699601138.5682722023-05-262023-06-17NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2023-06-062
663FZ12023-05-2712:35:14RFB88.585878-7.10670113.94038449.317333505.493066-195.852250102.26693772.9764422023-06-23R Arm/Elbow Strain151.070362140.9376462023-06-172023-05-1742.55258742.26307759.79638356.431541NaNNaNNaNNaNNaNNaNNaNNaN2023-06-062
763FZ12023-05-2712:35:14RFB88.585878-7.10670113.94038449.317333505.493066-195.852250102.26693772.9764422023-06-23R Arm/Elbow Strain151.070362140.9376462023-06-172023-06-17NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2023-06-062
863FZ22023-05-2712:35:38RCH83.471159-14.6245223.53980246.208194322.439931-197.942599101.57337872.4872352023-06-23R Arm/Elbow Strain140.789087133.0087552023-05-312023-05-1742.55258742.26307759.79638356.431541NaNNaNNaNNaNNaNNaNNaNNaN2023-06-062
963FZ22023-05-2712:35:38RCH83.471159-14.6245223.53980246.208194322.439931-197.942599101.57337872.4872352023-06-23R Arm/Elbow Strain140.789087133.0087552023-05-312023-06-17NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2023-06-062